Information matrix test

In econometrics, the information matrix test is used to determine whether a regression model is misspecified. The test was developed by Halbert White, who observed that in a correctly specified model and under standard regularity assumptions, the Fisher information matrix can be expressed in either of two ways: as the outer product of the gradient, or as a function of the Hessian matrix of the log-likelihood function.

Consider a linear model $\mathbf {y} =\mathbf {X} \mathbf {\beta } +\mathbf {u}$, where the errors $\mathbf {u}$ are assumed to be distributed $\mathrm {N} (0,\sigma ^{2}\mathbf {I} )$. If the parameters $\beta$ and $\sigma ^{2}$ are stacked in the vector $\mathbf {\theta } ^{\mathsf {T}}={\begin{bmatrix}\beta &\sigma ^{2}\end{bmatrix}}$, the resulting log-likelihood function is

$\ell (\mathbf {\theta } )=-{\frac {n}{2}}\log \sigma ^{2}-{\frac {1}{2\sigma ^{2}}}\left(\mathbf {y} -\mathbf {X} \mathbf {\beta } \right)^{\mathsf {T}}\left(\mathbf {y} -\mathbf {X} \mathbf {\beta } \right)$

The information matrix can then be expressed as

$\mathbf {I} (\mathbf {\theta } )=\operatorname {E} \left[\left({\frac {\partial \ell (\mathbf {\theta } )}{\partial \mathbf {\theta } }}\right)\left({\frac {\partial \ell (\mathbf {\theta } )}{\partial \mathbf {\theta } }}\right)^{\mathsf {T}}\right]$

that is the expected value of the outer product of the gradient or score. Second, it can be written as the negative of the Hessian matrix of the log-likelihood function

$\mathbf {I} (\mathbf {\theta } )=-\operatorname {E} \left[{\frac {\partial ^{2}\ell (\mathbf {\theta } )}{\partial \mathbf {\theta } \,\partial \mathbf {\theta } ^{\mathsf {T}}}}\right]$

If the model is correctly specified, both expressions should be equal. Combining the equivalent forms yields

$\mathbf {\Delta } (\mathbf {\theta } )=\sum _{i=1}^{n}\left[{\frac {\partial ^{2}\ell (\mathbf {\theta } )}{\partial \mathbf {\theta } \,\partial \mathbf {\theta } ^{\mathsf {T}}}}+{\frac {\partial \ell (\mathbf {\theta } )}{\partial \mathbf {\theta } }}{\frac {\partial \ell (\mathbf {\theta } )}{\partial \mathbf {\theta } }}\right]$

where $\mathbf {\Delta } (\mathbf {\theta } )$ is an $(r\times r)$ random matrix, where $r$ is the number of parameters. White showed that the elements of $n^{-1/2}\mathbf {\Delta } (\mathbf {\hat {\theta }} )$, where $\mathbf {\hat {\theta }}$ is the MLE, are asymptotically normally distributed with zero means when the model is correctly specified. In small samples, however, the test generally performs poorly.

Further reading

• Krämer, W.; Sonnberger, H. (1986). The Linear Regression Model Under Test. Heidelberg: Physica-Verlag. pp. 105–110. ISBN 3-7908-0356-1.
• White, Halbert (1994). "Information Matrix Testing". Estimation, Inference and Specification Analysis. New York: Cambridge University Press. pp. 300–344. ISBN 0-521-25280-6.